1. Chapter 1: The Database Environment and Development Process
Modern Database Management

2. Objectives Define terms
Name limitations of conventional file processing
Explain advantages of databases
Identify costs and risks of databases
List components of database environment
Identify categories of database applications
Describe database system development life cycle
Explain prototyping and agile development approaches
Explain roles of individuals
Explain the three-schema architecture for databases

3. Definitions Database: organized collection of logically related data
Data: stored representations of meaningful objects and events
Structured: numbers, text, dates
Unstructured: images, video, documents
Information: data processed to increase knowledge in the person using the data
Metadata: data that describes the properties and context of user data

4. Figure 1-1a Data in context
Context helps users understand data 4

5. Figure 1-1b Summarized data
Graphical displays turn data into useful information that managers can use for decision making and interpretation 5

6. Descriptions of the properties or characteristics of the data, including data types, field sizes, allowable values, and data context 6

7. Disadvantages of File Processing
Program-Data Dependence
All programs maintain metadata for each file they use
Duplication of Data
Different systems/programs have separate copies of the same data
Limited Data Sharing
No centralized control of data
Lengthy Development Times
Programmers must design their own file formats
Excessive Program Maintenance
80% of information systems budget

8. Problems with Data Dependency
Each application programmer must maintain his/her own data
Each application program needs to include code for the metadata of each file
Each application program must have its own processing routines for reading, inserting, updating, and deleting data
Lack of coordination and central control
Non-standard file formats

9. © 2013 Pearson Education, Inc. Publishing as Prentice Hall
Duplicate Data
© 2013 Pearson Education, Inc.  Publishing as Prentice Hall
Chapter 1 9

10. Problems with Data Redundancy
Waste of space to have duplicate data
Causes more maintenance headaches
The biggest problem:
Data changes in one file could cause inconsistencies
Compromises in data integrity

11. SOLUTION: The DATABASE Approach
Central repository of shared data
Data is managed by a controlling agent
Stored in a standardized, convenient form
Requires a Database Management System (DBMS)

12. Database Management System
A software system that is used to create, maintain, and provide controlled access to user databases
Order Filing
System
Central database
Contains employee,
order, inventory,
pricing, and
customer data
Invoicing
System
DBMS
Payroll
System
DBMS manages data resources like an operating system manages hardware resources

13. Advantages of THE DatabaSE APPROACH
Program-data independence
Planned data redundancy
Improved data consistency
Improved data sharing
Increased application development productivity
Enforcement of standards
Improved data quality
Improved data accessibility and responsiveness
Reduced program maintenance
Improved decision support

14. Costs and Risks of the Database Approach
New, specialized personnel
Installation and management cost and complexity
Conversion costs
Need for explicit backup and recovery
Organizational conflict

15. Elements of the Database Approach
Data models
Graphical system capturing nature and relationship of data
Enterprise Data Model–high-level entities and relationships for the organization
Project Data Model–more detailed view, matching data structure in database or data warehouse
Entities
Noun form describing a person, place, object, event, or concept
Composed of attributes
Relationships
Between entities
Usually one-to-many (1:M) or many-to-many (M:N)
Relational Databases
Database technology involving tables (relations) representing entities and primary/foreign keys representing relationships

16. Figure 1-3 Comparison of enterprise and project level data models
Segment of an enterprise data model
Segment of a project-level data model 16

17.  One-to-many relationship
One customer may place many orders, but each order is placed by a single customer
 One-to-many relationship 17

18.  One-to-many relationship
One order has many order lines; each order line is associated with a single order
 One-to-many relationship 18

19.  One-to-many relationship
One product can be in many order lines, each order line refers to a single product
 One-to-many relationship 19

20.  Many-to-many relationship
Therefore, one order involves many products and one product is involved in many orders
 Many-to-many relationship 20

21. 21

22. Figure 1-5 Components of the Database Environment

23. Components of the Database Environment
CASE Tools–computer-aided software engineering
Repository–centralized storehouse of metadata
Database Management System (DBMS) –software for managing the database
Database–storehouse of the data
Application Programs–software using the data
User Interface–text and graphical displays to users
Data/Database Administrators–personnel responsible for maintaining the database
System Developers–personnel responsible for designing databases and software
End Users–people who use the applications and databases

24. Enterprise Data Model First step in the database development process
Specifies scope and general content
Overall picture of organizational data at high level of abstraction
Entity-relationship diagram
Descriptions of entity types
Relationships between entities
Business rules

25. FIGURE 1-6 Example business function-to-data entity matrix25

26. Two Approaches to Database and IS Development
SDLC
System Development Life Cycle
Detailed, well-planned development process
Time-consuming, but comprehensive
Long development cycle
Prototyping
Rapid application development (RAD)
Cursory attempt at conceptual data modeling
Define database during development of initial prototype
Repeat implementation and maintenance activities with new prototype versions

27. Systems Development Life Cycle (see also Figure 1-7)
Planning
Analysis
Physical Design
Implementation
Maintenance
Logical Design

28. Systems Development Life Cycle (see also Figure 1-7) (cont.)
Planning
Planning
Analysis
Physical Design
Implementation
Maintenance
Logical Design
Purpose–preliminary understanding
Deliverable–request for study
Database activity–
enterprise modeling and early conceptual data modeling

29. Systems Development Life Cycle (see also Figure 1-7) (cont.)
Purpose–thorough requirements analysis and structuring
Deliverable–functional system specifications
Planning
Analysis
Physical Design
Implementation
Maintenance
Logical Design
Analysis
Database activity–thorough and integrated conceptual data modeling

30. Systems Development Life Cycle (see also Figure 1-7) (cont.)
Purpose–information requirements elicitation and structure
Deliverable–detailed design specifications
Planning
Analysis
Physical Design
Implementation
Maintenance
Logical Design
Logical Design
Database activity–
logical database design (transactions, forms, displays, views, data integrity and security)

31. Systems Development Life Cycle (see also Figure 1-7) (cont.)
Purpose–develop technology and organizational specifications
Deliverable–program/data structures, technology purchases, organization redesigns
Planning
Analysis
Physical Design
Implementation
Maintenance
Logical Design
Physical Design
Database activity–
physical database design (define database to DBMS, physical data organization, database processing programs)

32. Systems Development Life Cycle (see also Figure 1-7) (cont.)
Purpose–programming, testing, training, installation, documenting
Deliverable–operational programs, documentation, training materials
Planning
Analysis
Physical Design
Implementation
Maintenance
Logical Design
Database activity–
database implementation, including coded programs, documentation, installation and conversion
Implementation

33. Systems Development Life Cycle (see also Figure 1-7) (cont.)
Planning
Analysis
Physical Design
Implementation
Maintenance
Logical Design
Purpose–monitor, repair, enhance
Deliverable–periodic audits
Database activity–
database maintenance, performance analysis and tuning, error corrections
Maintenance

34. Prototyping Database Methodology (Figure 1-8)34

35. Prototyping Database Methodology (Figure 1-8) (cont.)35

36. Prototyping Database Methodology (Figure 1-8) (cont.)36

37. Prototyping Database Methodology (Figure 1-8) (cont.)

38. Prototyping Database Methodology (Figure 1-8) (cont.)38

39. Database Schema External Schema Conceptual Schema Internal Schema
User Views
Subsets of Conceptual Schema
Can be determined from business-function/data entity matrices
DBA determines schema for different users
Conceptual Schema
E-R models–covered in Chapters 2 and 3
Internal Schema
Logical structures–covered in Chapter 4
Physical structures–covered in Chapter 5

40. Figure 1-9 Three-schema architecture
Different people have different views of the database…these are the external schema
The internal schema is the underlying design and implementation 40

41. Managing Projects
Project–a planned undertaking of related activities to reach an objective that has a beginning and an end
Initiated and planned in planning stage of SDLC
Executed during analysis, design, and implementation
Closed at the end of implementation

42. Managing Projects: People Involved
Business analysts
Systems analysts
Database analysts and data modelers
Users
Programmers
Database architects
Data administrators
Project managers
Other technical experts

43. Evolution of Database Systems
Driven by four main objectives:
Need for program-data independence  reduced maintenance
Desire to manage more complex data types and structures
Ease of data access for less technical personnel
Need for more powerful decision support platforms

44. Figure 1-10a Evolution of database technologies

45. Figure 1-10b Database architectures

46. Figure 1-10b Database architectures (cont.)

47. Figure 1-10b Database architectures (cont.)

48. The Range of Database Applications
Personal databases
Two-tier and N-tier Client/Server databases
Enterprise applications
Enterprise resource planning (ERP) systems
Data warehousing implementations

49. Figure 1-11 Two-tier database with local
area network 49

50. Figure 1-12 Three-tiered client/server database architecture50

51. Enterprise Database Applications
Enterprise Resource Planning (ERP)
Integrate all enterprise functions (manufacturing, finance, sales, marketing, inventory, accounting, human resources)
Data Warehouse
Integrated decision support system derived from various operational databases

52. FIGURE 1-13 Computer
System for Pine Valley
Furniture Company 52